CN114417819A

CN114417819A - Data recall method and device, storage medium and electronic equipment

Info

Publication number: CN114417819A
Application number: CN202210083635.7A
Authority: CN
Inventors: 林轩; 李松; 张赣
Original assignee: Alipay Hangzhou Information Technology Co Ltd
Current assignee: Alipay Hangzhou Information Technology Co Ltd
Priority date: 2022-01-24
Filing date: 2022-01-24
Publication date: 2022-04-29

Abstract

The specification discloses a data recall method, a data recall device, a storage medium and electronic equipment, wherein the method comprises the following steps: when a content publishing event of a first content platform is monitored, content data corresponding to the content publishing event is obtained, whether the content data is data of a target type or not is judged, if the content data is the data of the target type, the content data is recalled to a second content platform, and content supply of the second content platform is increased.

Description

Data recall method and device, storage medium and electronic equipment

Technical Field

The present disclosure relates to the field of identity recognition technologies, and in particular, to a data recall method, an apparatus, a storage medium, and an electronic device.

Background

Along with popularization of investment and financing, more and more new investors are introduced into a financing market, but most of the new investors do not have financing experience and lack financing knowledge, the emotion of the new investors is easily influenced by market fluctuation, irrational investment behaviors are often generated, and loss is serious. Therefore, the enterprise constructs an investment education platform to guide the user to learn the financing knowledge.

Disclosure of Invention

The data recall method, apparatus, storage medium, and electronic device provided in the embodiments of this specification can recall content data applicable to a second content platform from a first content platform to enrich content supply of the second content platform. The technical scheme is as follows:

in a first aspect, a data recall method provided in an embodiment of the present specification includes:

when a content publishing event of a first content platform is monitored, content data corresponding to the content publishing event is obtained;

judging whether the content data is the data of the target type;

and if the content data is the data of the target type, recalling the content data to a second content platform.

In a second aspect, an embodiment of the present specification provides a data recall device, where the target data recall device includes:

the target data acquisition module is used for acquiring target touch operation data and target gesture action data when a target user operates a display screen of the electronic equipment;

the data acquisition module is used for acquiring content data corresponding to a content release event when the content release event of a first content platform is monitored;

the type judging module is used for judging whether the content data is the data of the target type;

and the data recall module is used for recalling the content data to a second content platform if the content data is the data of the target type.

In a third aspect, the present specification provides a storage medium storing at least one instruction, which is adapted to be loaded by a processor and to perform the above method steps.

In a fourth aspect, embodiments of the present specification provide an electronic device, which may include: a processor and a memory; wherein the memory stores at least one instruction adapted to be loaded by the processor and to perform the above-mentioned method steps.

The technical scheme provided by some embodiments of the present description brings beneficial effects at least including:

by adopting the data recall method provided by the embodiment of the specification, the content release event of the first content platform is monitored, when the content release event of the first content platform is monitored, the content data corresponding to the content release event is acquired, whether the content data is the data of the target type or not is judged, and if the content data is the data of the target type, the content data is recalled to the second content platform, so that the content supply of the second content platform is increased, and the content quantity and the category of the second content platform are enriched.

Drawings

In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present specification, and other drawings can be obtained by those skilled in the art without creative efforts.

Fig. 1 is a schematic flow chart of a data recall method according to an embodiment of the present disclosure;

FIG. 2 is a schematic diagram illustrating an example of a data recall provided by an embodiment of the present disclosure;

FIG. 3 is a flow chart illustrating a data recall method according to an embodiment of the present disclosure;

FIG. 4 is a flowchart illustrating a data recall method according to an embodiment of the present disclosure;

FIG. 5 is an exemplary diagram of a knowledge point framework provided by embodiments of the present disclosure;

fig. 6 is a diagram of a correspondence between knowledge points and key phrases provided in an embodiment of the present specification;

FIG. 7 is a flowchart illustrating a data recall method according to an embodiment of the present disclosure;

FIG. 8 is a flowchart of a data recall method provided by an embodiment of the present specification;

FIG. 9 is a schematic structural diagram of a data recall device according to an embodiment of the present disclosure;

fig. 10 is a schematic structural diagram of a type determining module provided in an embodiment of the present disclosure;

fig. 11 is a schematic structural diagram of a type determination unit provided in an embodiment of the present specification;

FIG. 12 is a schematic structural diagram of a data recall device according to an embodiment of the present disclosure;

fig. 13 is a schematic structural diagram of an electronic device provided in an embodiment of this specification.

Detailed Description

The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present specification without any creative effort belong to the protection scope of the present specification.

In the description herein, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In the description of the present specification, it is to be noted that, unless explicitly stated or limited otherwise, "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus. The specific meanings of the above terms in the present specification can be understood in specific cases by those of ordinary skill in the art. Further, in the description of the present specification, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.

The investment education platform is used for putting relevant articles or video contents of investment education, the content sources are mainly bought or invited for individual large-V production through operation, the updating frequency is slow, the diversity and the richness of the contents are all deficient, and the investment education platform is difficult to adapt to the learning requirements of investors of different levels.

In the prior art, most of the content supply of some investment education platforms comes from the arrangement of professional teams, so that manpower and material resources are consumed, the updating frequency is still low, and the updated content is not diverse enough.

Based on this, the present specification provides a data recall method, where a content release event of a first content platform is monitored, and when the content release event of the first content platform is monitored, content data corresponding to the content release event is obtained, whether the content data is target-type data is determined, and if the content data is the target-type data, the content data is recalled to a second content platform, so that content supply of the second content platform is increased, and the content quantity and the content category of the second content platform are enriched.

The following is a detailed description with reference to specific examples. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present specification. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the specification, as detailed in the appended claims. The flow diagrams depicted in the figures are merely exemplary and need not be performed in the order of the steps shown. For example, some steps are parallel, and there is no strict sequence relationship in logic, so the actual execution sequence is variable.

Please refer to fig. 1, which is a flowchart illustrating a data recall method according to an embodiment of the present disclosure. In a specific embodiment, the data recall method is applied to the data recall device and the electronic equipment configured with the data recall device. The specific process of the present embodiment will be described below by taking an electronic device as an example, and it should be understood that the electronic device applied in the present embodiment may be a smart phone, a tablet computer, a desktop computer, a wearable device, and the like, which is not limited herein. As will be described in detail with respect to the flow shown in fig. 1, the data recall method may specifically include the following steps:

s101, when a content release event of a first content platform is monitored, content data corresponding to the content release event is obtained;

specifically, a content publishing event of the first content platform is monitored based on the event monitoring function, and when the content publishing event of the first content platform is monitored, content data corresponding to the content publishing event is acquired.

The content data refers to content uploaded to the first content platform by a user, and the content data includes but is not limited to articles, videos, voices and the like.

In one embodiment, the first content platform may be a fund platform, a stock platform, an investment financing information platform, or the like.

In one embodiment, an event monitoring function is arranged on a data uploading interface of a first content platform to continuously monitor a content publishing event when a user uploads content data when the user publishes new content data on the first content platform, and when the event monitoring function monitors the content publishing event, the corresponding content data uploaded by the user is read from the first content platform. S102, judging whether the content data is data of a target type;

specifically, it is determined whether the acquired content data is data of a desired target type. In one embodiment, the type of the content data is determined by analyzing the semantic information and the keyword information of the content data, and then whether the content data is the required target type data is judged.

The target type of data refers to the type of data required by the second content platform.

In one embodiment, the target type may be an article, video, or audio containing investment education knowledge.

And S103, if the content data is the data of the target type, recalling the content data to a second content platform.

In the embodiment of the present specification, a content publishing event of a first content platform is monitored, content data published by the first content platform is acquired, and then it is determined whether to recall the content data to a second content platform according to a type of the content data, where the second content platform only recalls the content data of a target type. It should be understood that, in this specification embodiment, the number of the first content platforms may be multiple, that is, the second content platform may call back, to the second content platform, content data belonging to the target type published by each first content platform by supervising content publishing events of multiple first content platforms.

In one embodiment, the second content platform may be an investment education platform, the first content platform may be a fund trading platform, a stock trading platform, a financial news platform, or the like, and the target type is an investment education class, referred to as "teaching class".

If the content data is the target type data, it indicates that the content data is the content data required by the second content platform, and the content data may be recalled to the second content platform.

In one embodiment, the recalling the content data to the second content platform may be that the content data is copied at the first content platform and stored in a storage space corresponding to the second content platform.

In an embodiment, the recalling the content data to the second content platform may be to mark a teaching class tag for the content data, so that the second content platform may find the content data according to the teaching class tag, and publish the content data on the second content platform at any time. It should be understood that, if the first content platform corresponds to the server 1 and the second content platform corresponds to the server 2, the content data published by the user on the first content platform will be stored under the server 1, and by tagging the content data of the target type with the teaching class tag, when the second content platform needs the content data tagged with the teaching class tag under the server 1, the content data can be found according to the addressing of the teaching class tag and published on the second content platform. In one embodiment, the second content platform may include a fund posting forum, a stock posting forum, and a futures posting forum, and after the content data is recalled to the second content platform, the content data may be further classified, which forum the content data specifically belongs to is determined, and the content data is posted below the affiliated forum.

Please refer to fig. 2, which is a schematic diagram illustrating an example of data recall according to an embodiment of the present disclosure. As shown in the figure, the first content platform is a fund platform, the second content platform is an investment education platform, when the content data 1 published on the first content platform is the content data of the target type, the content data 1 is recalled to the second content platform, the second content platform comprises three versions of fund, stock and futures, and the content data 1 is released under the fund version according to the type of the content data 1.

Referring to fig. 3, a flow chart of a data recall method provided in the present disclosure is schematically illustrated. As shown in fig. 3, the data recall method may include the steps of:

s201, when a content release event of a first content platform is monitored, content data corresponding to the content release event is obtained;

In one embodiment, an event monitoring function is set in a data uploading interface of a first content platform to continuously monitor a content publishing event when a user publishes new content data on the first content platform, and when the event monitoring function monitors the content publishing event, the corresponding content data uploaded by the user is acquired.

For example, if the first content platform is a fund platform, when a user of the fund platform publishes content on the platform, a corresponding content publishing event will be monitored by the event monitoring function when the user publishes content data, and then content data corresponding to the content publishing event is acquired.

S202, extracting a first key text of the content data;

specifically, a first key text which may contain key information in the content data is extracted.

Optionally, if the content data is a video, the first key text may be a title of the article.

Optionally, if the content data is an article, the first keyword text may be a title of the article and the top 100 words of the article.

Optionally, if the content data is a voice, the first key text may be the first 20 sentences after the voice is converted into a text.

Optionally, the first key text may also be all texts of the content data, and the content data may be a video, an article, or an audio.

S203, performing word segmentation processing on the first key text to obtain at least one word;

specifically, word segmentation processing is performed on the first key text based on a word segmentation processing method, so that at least one word after word segmentation processing is obtained.

Optionally, the word segmentation processing method includes, but is not limited to: the method comprises a forward maximum matching method, a reverse maximum matching method, a word segmentation method based on an N-gram language model, a word segmentation method based on an HMM, a word segmentation method based on a CRF, an end-to-end word segmentation method based on deep learning and the like.

Optionally, after performing word segmentation processing on the first key text to obtain at least one word, the word segmentation processing method may further include performing word de-stop processing on the at least one word after the word segmentation processing to obtain at least one word. The stop word processing may remove functional words without actual meanings, such as yes, o, and the like, in the words after the first key text word segmentation processing, reduce the word segmentation result of the first key text, and reduce the calculation amount for the subsequent processing process.

S204, mapping each word to corresponding word vectors, and judging whether the content data is target type data or not based on each word vector;

specifically, each word is converted into a word vector representation based on a pre-trained word vector model, each word vector is input into a pre-trained classification model, the probability that the content data belongs to the target type data is obtained, and whether the content data is the target type data is judged according to the probability and a preset threshold.

Optionally, the word vector model may be any one of a word2vec word vector model, a GloVe word vector model, a deep learning-based vectorization method, an ELMo word vector model, and the like.

Optionally, the classification model is a deep learning-based neural network model, including but not limited to: BERT models, recurrent neural network models, convolutional neural network models, and the like.

In an embodiment, the determining whether the content data is the data of the target type according to the probability and a preset threshold includes: if the probability is larger than or equal to the preset threshold, determining that the content data is data of a target type; and if the probability is smaller than the preset threshold value, determining that the content data is not the data of the target type.

S205, if the content data is the data of the target type, recalling the content data to a second content platform;

specifically, if the content data is the target type data, it indicates that the content data is the content data required by the second content platform, and the content data may be recalled to the second content platform.

Optionally, the recalling the content data to the second content platform may be to establish a data transmission interface between the first content platform and the second content platform, and directly read the content data from the first content platform by the second content platform.

Optionally, the recalling the content data to the second content platform may also be that a data transmission interface between the first content platform and the second content platform is established, the second content platform directly accesses the database corresponding to the first content platform, copies the content data, and pastes the copied content data to the database corresponding to the second content platform, so that the second content platform can directly read the content data from the local database.

And S206, determining the knowledge points corresponding to the content data and mounting the knowledge points based on the knowledge points contained in the knowledge point frame.

Specifically, a knowledge point framework system is established on a second content platform, the knowledge point framework comprises a plurality of preset knowledge points, after the content data is recalled to the second content platform, the knowledge points corresponding to the content data are determined, and the corresponding knowledge points are mounted under the content data.

It can be understood that, in order to mount the corresponding knowledge point for the content data recalled to the second content platform, appropriate content data can be recommended to the user according to the knowledge point required by the user.

By adopting the data recall method provided by the embodiment of the specification, through monitoring the content release event of the first content platform, when the content release event of the first content platform is monitored, the content data corresponding to the content release event is obtained, then the first key text corresponding to the content data is extracted, word segmentation processing is performed on the first key text to obtain at least one word, each word is respectively mapped into a corresponding word vector, whether the content data is the data of a target type is judged based on each word vector, if the content data is the data of the target type, the content data is recalled back to the second content platform, the content supply of the second content platform is increased, the content quantity and the quality of the second content platform are enriched, and the content update frequency of the second content platform is improved; and according to the knowledge points contained in the set knowledge point frame, mounting corresponding knowledge points for the content data of the second content platform, so that the content data suitable for the user to read is recommended to the user according to the knowledge points corresponding to the content data and the requirements of the user.

Please refer to fig. 4, which is a flowchart illustrating a data recall method according to an embodiment of the present disclosure.

As shown in fig. 4, the data recall method may include the steps of:

s301, when a content release event of a first content platform is monitored, content data corresponding to the content release event is obtained;

specifically, please refer to the content in step S201 in step S301, which is not described herein.

S302, extracting a first key text of the content data;

specifically, please refer to the content in step S202 in step S302, which is not described herein.

S303, performing word segmentation processing on the first key text to obtain at least one word;

specifically, please refer to the contents in step S203 in step S303, which is not described herein.

S304, mapping each word to a corresponding word vector respectively, and judging whether the content data is target type data or not based on each word vector;

specifically, please refer to the content in step S204 together in step S304, which is not described herein again.

S305, if the content data is the data of the target type, recalling the content data to a second content platform;

specifically, please refer to the content in step S205 in step S305, which is not described herein again.

S306, extracting a second key text of the content data;

specifically, the title and the first text sentence of the content data are extracted, and the title and the first text sentence of the content data are used as the second key text of the content data.

Optionally, the second key text may be a title and an entire body of the content data.

S307, matching the second key text with key phrases corresponding to the knowledge points in the knowledge point frame;

and S308, taking the knowledge points corresponding to the successfully matched key phrases as the knowledge points corresponding to the content data and mounting the knowledge points.

Specifically, a corresponding key phrase is set for each knowledge point in a knowledge point frame in advance, a second key text is matched with the key phrases corresponding to each knowledge point, if the second key text is successfully matched with the key phrases, it is indicated that the content data includes the knowledge point corresponding to the key phrase, and then the knowledge point corresponding to the key phrase is mounted under the content data.

Please refer to fig. 5, which is a schematic diagram illustrating an example knowledge point framework according to an embodiment of the present disclosure. As shown in fig. 5, the knowledge point framework includes a plurality of primary knowledge points, each of which includes a plurality of secondary knowledge points, each of which includes a plurality of knowledge points, as shown in the figure, knowledge points 1 to n.

Further, please refer to fig. 6, which is a diagram of correspondence between knowledge points and key phrases provided in the embodiments of the present specification. For example, if the knowledge point 1 is "fund definition", the corresponding key phrase 1 may be "how to choose the fund, what the fund is, fund + scale, fund + how to choose"; for another example, if the knowledge point 2 is "macro economic risk", the corresponding key phrase 2 may be "macro economic risk + what/how to evaluate, macro economic risk + feature, macro economic risk + countermeasure, macro economic risk + type".

That is, each knowledge point in the knowledge point frame has a corresponding keyword group, and the knowledge point corresponding to the content data can be obtained by matching the content data with the keyword group, it can be understood that one content data can correspond to a plurality of knowledge points.

It should be noted that by determining the knowledge points corresponding to the content data and mounting the corresponding knowledge points for the content data, the content depth of the content data can be determined according to the knowledge points corresponding to the content data, so as to push appropriate content data for users of different levels.

In one embodiment, the second content platform is an investment education platform, the content data is investment education articles, the knowledge point frames are knowledge point frames of investment education, each investment education article corresponds to at least one knowledge point in the knowledge point frames, and the content depth of the investment education articles can be determined according to the knowledge point corresponding to the investment education article.

In one embodiment, the investment education level of the user can be determined according to the historical browsing records of the user on the investment education platform, and the investment education articles with the content depth corresponding to the investment education level of the user are pushed for the user according to the investment education level of the user.

Optionally, in an embodiment, the matching of the second key text with the key word group corresponding to each knowledge point in the knowledge point frame may further be performed by obtaining a first word vector corresponding to the second key text based on a word vector model, obtaining a second word vector corresponding to the key word group based on the word vector model, calculating cosine similarity between the first word vector and the second word vector, and if the cosine similarity between the first word vector and the second word vector is greater than or equal to a preset cosine similarity threshold, indicating that the matching of the content data corresponding to the second key text and the knowledge point corresponding to the key word group is successful, otherwise, failing to match.

Optionally, in an embodiment, if one piece of content data includes multiple knowledge points at the same time, a weight is set for each knowledge point according to cosine similarity between a second word vector corresponding to each knowledge point and a first word vector corresponding to the content data, and the higher the cosine similarity is, the larger the weight of the corresponding knowledge point is.

Referring to fig. 7, a flow chart of a data recall method according to an embodiment of the present disclosure is shown. As shown in fig. 7, the data recall method may include the steps of:

s401, when a content publishing event of a first content platform is monitored, content data corresponding to the content publishing event is obtained;

s402, extracting a first key text of the content data;

s403, performing word segmentation processing on the first key text to obtain at least one word;

s404, mapping each word to a corresponding word vector, and judging whether the content data is target type data or not based on each word vector;

s405, if the content data is the data of the target type, recalling the content data to a second content platform;

s406, inputting the content data into a pre-trained knowledge point classification model, outputting knowledge points corresponding to the content data and mounting.

Specifically, the pre-training of the knowledge point classification model based on deep learning is performed, content data are input into the pre-trained knowledge point classification model, the knowledge point classification model outputs knowledge points corresponding to the content data, and the knowledge points output by the knowledge point classification model are mounted under the content data.

Further, the knowledge point classification model is generated based on sample content data training of a large number of target types. Specifically, a corresponding knowledge point is mounted for each sample content data, and each sample content data and the corresponding knowledge point are input to a knowledge point classification model for training.

Alternatively, the knowledge point classification model may be a textcnn model or other mature deep learning based neural network model.

Referring to fig. 8, a flowchart of a data recall method according to an embodiment of the present disclosure is shown. As shown in fig. 8, the data recall method includes:

s1, monitoring a content publishing event of the first content platform;

specifically, a content publishing event of the first content platform is monitored through an event monitoring function.

S2, acquiring content data corresponding to the content release event;

specifically, when a content distribution event of the first content platform is monitored, content data corresponding to the content distribution event is acquired.

S3, judging the type of the content data;

specifically, whether the content data corresponding to the content publishing event is the target type content data required by the second content platform is judged.

And if the content data corresponding to the content publishing event is not the content data of the target type required by the second content platform, abandoning the content data and continuing to monitor the content publishing event of the first content platform.

And if the content data corresponding to the content publishing event is the target type content data required by the second content platform, recalling the content data to the second content platform.

S4, recalling to the second content platform;

s5, the knowledge point is mounted for the content data.

Specifically, the knowledge point is mounted for the content data of the second content platform.

By adopting the data recall method provided by the embodiment of the specification, the content release event of the first content platform is monitored, when the content release event of the first content platform is monitored, the content data corresponding to the content release event is obtained, whether the content data is the data of the target type or not is judged, if the content data is the data of the target type, the content data is recalled to the second content platform, the content supply of the second content platform is increased, the content quantity and the content category of the second content platform are enriched, and the content update frequency of the second content platform is improved; and according to the knowledge points contained in the set knowledge point frame, mounting corresponding knowledge points for the content data of the second content platform, so that the content data suitable for the user to read is recommended to the user according to the knowledge points corresponding to the content data and the requirements of the user.

Fig. 9 is a schematic structural diagram of a data recall device according to an embodiment of the present disclosure. As shown in fig. 9, the data recalling apparatus 1 can be implemented by software, hardware or a combination of both as all or a part of an electronic device. According to some embodiments, the data recall apparatus 1 includes a data obtaining module 11, a type determining module 12, and a data recall module 13, and specifically includes:

the data acquisition module 11 is configured to acquire content data corresponding to a content publishing event when the content publishing event of a first content platform is monitored;

a type judging module 12, configured to judge whether the content data is target type data;

a data recall module 13, configured to recall the content data to a second content platform if the content data is the data of the target type.

Optionally, please refer to fig. 10, which is a schematic structural diagram of a type determining module provided in the embodiments of the present specification. As shown in fig. 10, the type determining module 12 includes:

a first text extraction unit 121 configured to extract a first key text of the content data;

a word segmentation processing unit 122, configured to perform word segmentation processing on the first key text to obtain at least one word;

a type determining unit 123, configured to map each word language into a corresponding word vector, and determine whether the content data is data of a target type based on each word vector.

Optionally, please refer to fig. 11, which is a schematic structural diagram of a type determining unit provided in the embodiments of the present specification. As shown in fig. 11, the type determining unit 123 includes:

a probability obtaining subunit 1231, configured to input each word vector into a pre-trained classification model, so as to obtain a probability that the content data belongs to data of a target type;

a type determining subunit 1232, configured to determine whether the content data is the data of the target type based on the probability and a preset threshold.

Optionally, the type determining subunit 1232 is specifically configured to:

if the probability is larger than or equal to the preset threshold, determining that the content data is data of a target type;

and if the probability is smaller than the preset threshold value, determining that the content data is not the data of the target type.

Optionally, please refer to fig. 12, which provides a schematic structural diagram of a data recall device according to an embodiment of the present disclosure. As shown in fig. 12, the data recall apparatus further includes:

and the knowledge point mounting module 14 is configured to determine a knowledge point corresponding to the content data based on the knowledge points included in the knowledge point frame, and mount the knowledge point.

Optionally, the knowledge point mounting module 14 is specifically configured to:

extracting second key texts of the content data;

matching the second key text with key word groups corresponding to the knowledge points in the knowledge point frame;

and taking the knowledge points corresponding to the successfully matched key phrases as the knowledge points corresponding to the content data and carrying out mounting.

and inputting the content data into a pre-trained knowledge point classification model, outputting knowledge points corresponding to the content data and mounting.

The above example numbers are for description only and do not represent the merits of the examples.

The data recall device provided by the embodiment of the present specification can execute the data recall method provided by the above embodiment, by monitoring a content publishing event of a first content platform, when the content publishing event of the first content platform is monitored, acquiring content data corresponding to the content publishing event, and determining whether the content data is target type data, if the content data is the target type data, recalling the content data to a second content platform, so that content supply of the second content platform is increased, the content quantity and the content category of the second content platform are enriched, and the content update frequency of the second content platform is increased; and according to the knowledge points contained in the set knowledge point frame, mounting corresponding knowledge points for the content data of the second content platform, so that the content data suitable for the user to read is recommended to the user according to the knowledge points corresponding to the content data and the requirements of the user.

In an embodiment of the present specification, a computer storage medium may store a plurality of instructions, where the instructions are suitable for being loaded by a processor and for executing the data recall method according to the embodiment shown in fig. 1 to 8, and a specific execution process may refer to specific descriptions of the embodiment shown in fig. 1 to 8, which is not described herein again.

A computer program product further provided in this specification stores at least one instruction, where the at least one instruction is loaded by the processor and executes the data recall method according to the embodiment shown in fig. 1 to 8, and a specific execution process may refer to a specific description of the embodiment shown in fig. 1 to 8, which is not described herein again.

Referring to fig. 13, a block diagram of an electronic device according to an exemplary embodiment of the present disclosure is shown. The electronic device in this specification may include one or more of the following components: a processor 110, a memory 120, an input device 130, an output device 140, and a bus 150. The processor 110, memory 120, input device 130, and output device 140 may be connected by a bus 150.

Processor 110 may include one or more processing cores. The processor 110 connects various parts within the overall electronic device using various interfaces and lines, and performs various functions of the electronic device 100 and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 120 and calling data stored in the memory 120. Alternatively, the processor 110 may be implemented in hardware using at least one of Digital Signal Processing (DSP), field-programmable gate Array (FPGA), and Programmable Logic Array (PLA). The processor 110 may integrate one or more of a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a modem, and the like. Wherein, the CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing display content; the modem is used to handle wireless communications. It is understood that the modem may not be integrated into the processor 110, but may be implemented by a communication chip.

The Memory 120 may include a Random Access Memory (RAM) or a read-only Memory (ROM). Optionally, the memory 120 includes a non-transitory computer-readable medium. The memory 120 may be used to store instructions, programs, code sets, or instruction sets.

The input device 130 is used for receiving input instructions or data, and the input device 130 includes, but is not limited to, a keyboard, a mouse, a camera, a microphone, or a touch device. The output device 140 is used for outputting instructions or data, and the output device 140 includes, but is not limited to, a display device, a speaker, and the like. In the embodiment of the present disclosure, the input device 130 may be a temperature sensor for acquiring an operating temperature of the electronic device. The output device 140 may be a speaker for outputting audio signals.

In addition, those skilled in the art will appreciate that the configurations of the electronic devices illustrated in the above-described figures do not constitute limitations on the electronic devices, which may include more or fewer components than illustrated, or some components may be combined, or a different arrangement of components. For example, the electronic device further includes a radio frequency circuit, an input unit, a sensor, an audio circuit, a wireless fidelity (WiFi) module, a power supply, a bluetooth module, and other components, which are not described herein again.

In the embodiment of the present specification, the execution subject of each step may be the electronic device described above. Optionally, the execution subject of each step is an operating system of the electronic device. The operating system may be an android system, an IOS system, or another operating system, which is not limited in this specification.

In the electronic device of fig. 13, the processor 110 may be configured to call a data recall program stored in the memory 120 and execute to implement a data recall method according to various method embodiments of the present description.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, and the program can be stored in a computer readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a read-only memory or a random access memory.

The above disclosure is only for the purpose of illustrating the preferred embodiments of the present disclosure, and it is not intended to limit the scope of the present disclosure, so that the present disclosure will be covered by the claims and their equivalents.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

Claims

1. A method of data recall, the method comprising:

judging whether the content data is the data of the target type;

2. The method of claim 1, wherein said determining whether the content data is of a target type comprises:

extracting first key texts of the content data;

performing word segmentation processing on the first key text to obtain at least one word;

and mapping each word to a corresponding word vector respectively, and judging whether the content data is the data of the target type or not based on each word vector.

3. The method of claim 1, wherein said determining whether the content data is of a target type based on each of the word vectors comprises:

inputting each word vector into a pre-trained classification model to obtain the probability that the content data belongs to the data of the target type;

and judging whether the content data is the data of the target type or not based on the probability and a preset threshold value.

4. The method of claim 3, wherein the determining whether the content data is of a target type based on the probability and a preset threshold comprises:

5. The method of claim 1, after recalling the content data to a second content platform, further comprising:

and determining the knowledge points corresponding to the content data and mounting the knowledge points based on the knowledge points contained in the knowledge point framework.

6. The method according to claim 5, wherein the determining and mounting the knowledge points corresponding to the content data based on the knowledge points included in the knowledge point framework comprises:

extracting second key texts of the content data;

7. The method according to claim 5, wherein the determining and mounting the knowledge points corresponding to the content data based on the knowledge points included in the knowledge point framework comprises:

8. A data recall apparatus, the apparatus comprising:

9. A storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, performs the steps of the method of any one of claims 1 to 7.

10. An electronic device, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the steps of the method according to any of claims 1-7.